Safe Harbor Statement

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2 Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle. 2

3 MySQL Optimizer: What s New in 8.0 Øystein Grøvlen Senior Principal Software Engineer MySQL Optimizer Team, Oracle May, 2018

4 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 4

5 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 5

6 Common Table Expression Alternative to derived table A derived table is a subquery in the FROM clause SELECT FROM (subquery) AS derived, t1... Common Table Expression (CTE) is just like a derived table, but its declaration is put before the query block instead of in FROM clause WITH derived AS (subquery) SELECT FROM derived, t1... A CTE may precede SELECT/UPDATE/DELETE including sub-queries WITH derived AS (subquery) DELETE FROM t1 WHERE t1.a IN (SELECT b FROM derived); 6

7 Common Table Expression vs Derived Table Better readability Can be referenced multiple times Can refer to other CTEs Improved performance 7

8 Better Readability Derived table: CTE: SELECT FROM t1 LEFT JOIN ((SELECT FROM ) AS dt JOIN t2 ON ) ON WITH dt AS (SELECT... FROM...) SELECT... FROM t1 LEFT JOIN (dt JOIN t2 ON...) ON... 8

9 Can Be Referenced Multiple Times Derived table can not be referenced twice: CTE can: SELECT... FROM (SELECT a, b, SUM(c) s FROM t1 GROUP BY a, b) AS d1 JOIN (SELECT a, b, SUM(c) s FROM t1 GROUP BY a, b) AS d2 ON d1.b = d2.a; WITH d AS (SELECT a, b, SUM(c) s FROM t1 GROUP BY a, b) SELECT... FROM d AS d1 JOIN d AS d2 ON d1.b = d2.a; Better performance with materialization: Multiple references only materialized once Derived tables and views will be materialized once per reference. 9

10 Can Refer to Other CTEs Derived tables can not refer to other derived tables: SELECT FROM (SELECT FROM ) AS d1, (SELECT FROM d1 ) AS d2 ERROR: 1146 (42S02): Table db.d1 doesn t exist CTEs can refer other CTEs: WITH d1 AS (SELECT FROM ), d2 AS (SELECT FROM d1 ) SELECT FROM d1, d2 10

11 Recursive CTE WITH RECURSIVE cte AS ( SELECT... FROM table_name /* "seed" SELECT */ UNION [DISTINCT ALL] SELECT... FROM cte, table_name ) /* "recursive" SELECT */ SELECT... FROM cte; A recursive CTE refers to itself in a subquery The seed SELECT is executed once to create the initial data subset, the recursive SELECT is repeatedly executed to return subsets of. Recursion stops when an iteration does not generate any new rows To limit recursion, set cte_max_recursion_depth Useful to dig in hierarchies (parent/child, part/subpart) 11

12 Recursive CTE A simple example Print 1 to 10 : WITH RECURSIVE qn AS ( SELECT 1 AS a UNION ALL SELECT 1+a FROM qn WHERE a<10 ) SELECT * FROM qn; a

13 Hierarchy Traversal Employee database CREATE TABLE employees ( id INT PRIMARY KEY, name VARCHAR(100), manager_id INT, FOREIGN KEY (manager_id) REFERENCES employees(id) ); INSERT INTO employees VALUES (333, "Yasmina", NULL), # CEO (198, "John", 333), # John reports to 333 (692, "Tarek", 333), (29, "Pedro", 198), (4610, "Sarah", 29), (72, "Pierre", 29), (123, "Adil", 692); 13

14 Hierarchy Traversal List reporting chain WITH RECURSIVE emp_ext (id, name, path) AS ( SELECT id, name, CAST(id AS CHAR(200)) FROM employees WHERE manager_id IS NULL UNION ALL SELECT s.id, s.name, CONCAT(m.path, ",", s.id) FROM emp_ext m JOIN employees s ON m.id=s.manager_id ) SELECT * FROM emp_ext ORDER BY path; id name path 333 Yasmina John 333, Tarek 333, Pedro 333,198, Adil 333,692, Sarah 333,198,29, Pierre 333,198,29,72 14

15 Hierarchy Traversal List reporting chain WITH RECURSIVE emp_ext (id, name, path) AS ( SELECT id, name, CAST(id AS CHAR(200)) FROM employees WHERE manager_id IS NULL UNION ALL SELECT s.id, s.name, CONCAT(m.path, ",", s.id) FROM emp_ext m JOIN employees s ON m.id=s.manager_id ) SELECT * FROM emp_ext ORDER BY path; id name path 333 Yasmina John 333, Pedro 333,198, Sarah 333,198,29, Pierre 333,198,29, Tarek 333, Adil 333,692,123 15

16 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 16

17 Window Functions: What Are They? A window function performs a calculation across a set of rows that are related to the current row, similar to an aggregate function. But unlike aggregate functions, a window function does not cause rows to become grouped into a single output row. Window functions can access values of other rows in the vicinity of the current row Aggregate function Window function 17

18 Window Function Example Sum up total salary for each department: SELECT name, dept_id, salary, SUM(salary) OVER (PARTITION BY dept_id) AS dept_total FROM employee ORDER BY dept_id, name; The OVER keyword signals a window function PARTITION == disjoint set of rows in result set name dept_id salary dept_total Newt NULL Dag 10 NULL Ed Fred Jon Michael Newt Lebedev Pete Jeff Will

19 Window Function Example: Frames name dept_id salary total Newt NULL Dag 10 NULL NULL Ed Fred Jon Michael Newt Lebedev Pete Jeff Will SELECT name, dept_id, salary, SUM(salary) OVER (PARTITION BY dept_id ORDER BY name ROWS 2 PRECEDING) total FROM employee ORDER BY dept_id, name; moving window frame: SUM(salary)... ROWS 2 PRECEDING A frame is a subset of a partition ORDER BY name within each partition 19

20 Window Function Example: Frames name dept_id salary total Newt NULL Dag 10 NULL NULL Ed Fred Jon Michael Newt Lebedev Pete Jeff Will SELECT name, dept_id, salary, SUM(salary) OVER (PARTITION BY dept_id ORDER BY name ROWS 2 PRECEDING) total FROM employee ORDER BY dept_id, name; 20

21 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 21

utf8mb4_0900_ai_ci default collation Accent and case sensitive

22 Improved UTF-8 Support in MySQL 8.0 Support for the latest Unicode 9.0 utf8mb4 made default character set! utf8mb4_0900_ai_ci default collation Accent and case sensitive collations Including 20+ language specific collations Now also Japanese and Russian Significantly improved performance 22

23 What Is in MySQL 5.7 and Earlier Versions? Default charset is latin1 and default collation is latin1_swedish_ci utf8 = utf8mb3: support BMP only utf8mb4 character set: Only accent and case insensitive collations Default collation is utf8mb4_general_ci, compares all characters beyond BMP, e.g. emojis, to be equal 20+ language specific collations Recommend to use: utf8mb4_unicode_520_ci 23

24 New Default Character Set No changes to existing tables Only has effect on new tables/schemas where character set is not explicitly defined. Separating character set/collation change from server upgrade Upgrade first, change charset/collation afterwards Recommend users to not mixing collations Error Illegal mix of collations Slower query because index can no longer be used 24

25 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 25

GIS Geography Support longitude/latitude Spatial Reference Systems (SRS) Support 5000 SRIDs from EPSG Geodetic Parameter Dataset SRID 4326 = WGS 84 ( GPS coordinates )

26 GIS Geography Support longitude/latitude Spatial Reference Systems (SRS) Support 5000 SRIDs from EPSG Geodetic Parameter Dataset SRID 4326 = WGS 84 ( GPS coordinates ) INFORMATION_SCHEMA.ST_SPATIAL_REFERENCE_SYSTEM SRID aware spatial datatypes, indexes, and functions Information Schema views ST_GEOMETRY_COLUMNS GeoHash(), GeoJSON() 26

27 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 27

28 SELECT... FOR UPDATE SKIP LOCKED Common problem: Hot row contention, multiple worker threads accessing the same rows Solution 1: Only read rows that are not locked InnoDB skips a locked row, and the next one goes to the result set Example: Booking system: Skip orders that are pending START TRANSACTION; SELECT * FROM seats WHERE seat_no BETWEEN 2 AND 3 AND booked = 'NO' FOR UPDATE SKIP LOCKED; 28

29 SELECT FOR UPDATE NOWAIT Common problem: Hot row contention, multiple worker threads accessing the same rows Solution 2: If any of the rows are already locked, the statement should fail immediately Without NOWAIT, have to wait for innodb_lock_wait_timeout (default: 50 sec) while trying to acquire lock Usage: START TRANSACTION; SELECT * FROM seats WHERE seat_no BETWEEN 2 AND 3 AND booked = 'NO' FOR UPDATE NOWAIT; ERROR 3572 (HY000): Statement aborted because lock(s) could not be acquired 29

30 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 30

31 JSON Functions MySQL 5.7 and 8.0 JSON_ARRAY_APPEND() JSON_ARRAY_INSERT() JSON_ARRAY() JSON_CONTAINS_PATH() JSON_CONTAINS() JSON_DEPTH() JSON_EXTRACT() JSON_INSERT() JSON_KEYS() JSON_LENGTH() JSON_MERGE[_PRESERVE]() JSON_OBJECT() JSON_QUOTE() JSON_REMOVE() JSON_REPLACE() JSON_SEARCH() JSON_SET() JSON_TYPE() JSON_UNQUOTE() JSON_VALID() JSON_PRETTY() JSON_STORAGE_SIZE() JSON_STORAGE_FREE() JSON_ARRAYAGG() JSON_OBJECTAGG() JSON_MERGE_PATCH() JSON_TABLE() 31

32 JSON Data Type CREATE TABLE t1(json_col JSON); INSERT INTO t1 VALUES ( '{ "people": [ { "name":"john Smith", "address":"780 Mission St, San Francisco, CA 94103"}, { "name":"sally Brown", "address":"75 37th Ave S, St Cloud, MN 94103"}, { "name":"john Johnson", "address":"1262 Roosevelt Trail, Raymond, ME 04071"} ); ] }' 32

33 JSON_TABLE Convert JSON documents to relational tables SELECT people.* FROM t1, JSON_TABLE(json_col, '$.people[*]' COLUMNS ( name VARCHAR(40) PATH '$.name', address VARCHAR(100) PATH '$.address')) people; name address John Smith 780 Mission St, San Francisco, CA Sally Brown 75 37th Ave S, St Cloud, MN 9410 John Johnson 1262 Roosevelt Trail, Raymond, ME

34 JSON_TABLE Filter JSON data SELECT people.* FROM t1, JSON_TABLE(json_col, '$.people[*]' COLUMNS ( name VARCHAR(40) PATH '$.name', address VARCHAR(100) PATH '$.address')) people; WHERE people.name LIKE 'John%'; name address John Smith 780 Mission St, San Francisco, CA John Johnson 1262 Roosevelt Trail, Raymond, ME

35 JSON_TABLE Nested Arrays [ ] { "father":"john", "mother":"mary", "marriage_date":" ", "children": [ { "name":"eric", "age":12 }, { "name":"beth", "age":10 } ] }, { "father":"paul", "mother":"laura", "children": [ { "name":"sarah", "age":9}, { "name":"noah", "age":3}, { "name":"peter", "age":1} ] } id father married child_id child age 1 John 1 1 Eric 12 1 John 1 2 Beth 10 2 Paul 0 1 Sarah 9 2 Paul 0 2 Noah 3 2 Paul 0 3 Peter 1 35

36 JSON_TABLE Nested Arrays JSON_TABLE (families, '$[*]' COLUMNS ( id FOR ORDINALITY, father VARCHAR(30) PATH '$.father', married INTEGER EXISTS PATH '$.marriage_date', NESTED PATH '$.children[*]' COLUMNS ( child_id FOR ORDINALITY, child VARCHAR(30) PATH '$.name', age INTEGER PATH '$.age ) ) id father married child_id child age 1 John 1 1 Eric 12 1 John 1 2 Beth 10 2 Paul 0 1 Sarah 9 2 Paul 0 2 Noah 3 2 Paul 0 3 Peter 1 36

37 JSON_TABLE SQL aggregation on JSON data SELECT father, COUNT(*) "#children", AVG(age) "age average" FROM t, JSON_TABLE (families, '$[*]' COLUMNS ( id FOR ORDINALITY, father VARCHAR(30) PATH '$.father', NESTED PATH '$.children[*]' COLUMNS (age INTEGER PATH '$.age ) ) AS fam GROUP BY id, father; father #children age average John Paul

38 JSON Aggregation Combine JSON documents in multiple rows into a JSON array CREATE TABLE t1 (id INT, grp INT, jsoncol JSON); INSERT INTO t1 VALUES (1, 1, '{"key1":"value1","key2":"value2"}'); INSERT INTO t1 VALUES (2, 1, '{"keya":"valuea","keyb":"valueb"}'); SELECT JSON_ARRAYAGG(jsoncol) FROM t1; [{"key1":"value1","key2":"value2"}, {"keya":"valuea","keyb":"valueb"}, {"keyx":"valuex","keyy":"valuey"}] INSERT INTO t1 VALUES (3, 2, '{"keyx":"valuex","keyy":"valuey"}'); 38

39 JSON Aggregation Put IDs into objects SELECT JSON_ARRAYAGG(JSON_MERGE_PATCH(JSON_OBJECT("id", id), family)) AS families FROM t, JSON_TABLE (families, '$[*]' COLUMNS ( id FOR ORDINALITY, family JSON PATH '$')) fam; families [{"id": 1, "father": "John", "mother": "Mary", "children": [{"age": 12, "name": "Eric"}, {"age": 10, "name": "Beth"}], "marriage_date": " "}, {"id": 2, "father": "Paul", "mother": "Laura", "children": [{"age": 9, "name": "Sarah"}, {"age": 3, "name": "Noah"}, {"age": 1, "name": "Peter"}]}] 39

40 JSON Aggregation Combine JSON documents in multiple rows into a JSON object CREATE TABLE t1 (id INT, grp INT, jsoncol JSON); INSERT INTO t1 VALUES (1, 1, '{"key1":"value1","key2":"value2"}'); INSERT INTO t1 VALUES (2, 1, '{"keya":"valuea","keyb":"valueb"}'); SELECT grp, JSON_OBJECTAGG(id, jsoncol) FROM t1 GROUP BY grp; 1 {"1":{"key1":"value1","key2":"value2"}, "2":{"keyA":"valueA","keyB":"valueB"}} 2 {"3":{"keyX":"valueX","keyY":"valueY"}} INSERT INTO t1 VALUES (3, 2, '{"keyx":"valuex","keyy":"valuey"}'); 40

41 JSON Utility Functions JSON_PRETTY(json_value) Pretty-print the JSON value JSON_STORAGE_SIZE(json_value) The number of bytes used to store the binary representation of a JSON document JSON_STORAGE_FREE(json_value) The number of bytes in its binary representation that is currently not used. The binary representation may have unused space after a JSON column was updated in-place using JSON_SET() 41

42 Partial Update of JSON In 5.7: Updating a JSON document always causes the entire document to be written In 8.0: Automatically detect potential in-place updates JSON_SET, JSON_REPLACE, JSON_REMOVE Input and target columns are the same Updating existing values, not adding new ones New value is equal to or smaller than the original value, or previous partial update has left sufficient space In 8.0: Only diff is shipped through binlog (default full image) In 8.0: InnoDB supports partial update of BLOB 42

43 Indexing JSON Array Upcoming! 5.7: Single value index, one index record <-> one data record Multi-value index for efficient queries against array fields Use CAST( <expr> AS <type> ARRAY) to create multi-valued index Expr is any expression that returns JSON array Type is any SQL type supported by CAST() 43

44 Indexing JSON Array id Families 1 {"father":"john", "mother":"mary", "marriage_date":" ", "children": [ {"name":"eric","age":12,"eyes":"blue"}, {"name":"beth","age":10, "eyes": "green"}]} 2 {"father":"paul", "mother": "Laura", "children": [ {"name":"sarah","age":9,"eyes":"brown"}, {"name":"noah","age":3,"eyes": "green"}, {"name":"peter","age":1,"eyes": "green"}]} Upcoming! CREATE TABLE t (families JSON); ALTER TABLE t ADD INDEX eye_color ((CAST(families->>"$.children[*].eyes" AS CHAR(10) ARRAY))) eye_color id blue 1 brown 2 green 1 green 2 44

45 Indexing JSON Array Upcoming! Find parents with children having brown eyes: SELECT id, families->>"$.father", families->>"$.mother" FROM t WHERE "brown" IN (families->>"$.children[*].eyes"); id father mother 2 Paul Laura eye_color id blue 1 brown 2 green 1 green 2 45

46 Indexing JSON Array Upcoming! Find parents with children having either blue or brown eyes. SELECT id, families->>"$.father", families->>"$.mother" FROM t WHERE JSON_OVERLAPS(families->>"$.children[*].eyes", '["blue", "brown"]') id father mother 1 John Mary 2 Paul Laura eye_color id blue 1 brown 2 green 1 green 2 46

47 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 47

48 Invisible Index Index is maintained by the SE, but ignored by the Optimizer Primary key cannot be INVISIBLE Use case: Check for performance drop BEFORE dropping an index ALTER TABLE t1 ALTER INDEX idx INVISIBLE; mysql> SHOW INDEXES FROM t1; Table Key_name Column_name Visible t1 idx a NO To see an invisible index: set optimizer_switch='use_invisible_indexes=on'; 48

49 Descending Index CREATE TABLE t1 ( a INT, b INT, INDEX a_b (a DESC, b ASC) ); In 5.7: Index in ascending order is created, server scans it backwards In 8.0: Index in descending order is created, server scans it forwards Works on B-tree indexes only Benefits: Use indexes instead of filesort for ORDER BY clause with ASC/DESC sort key Forward index scan is slightly faster than backward index scan 49

50 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 50

51 Motivation for Improving the MySQL Cost Model Produce more correct cost estimates Better decisions by the optimizer should improve performance Adapt to new hardware architectures SSD, larger memories, caches More maintainable cost model implementation Avoid hard coded cost constants Refactoring of existing cost model code Configurable and tunable Faster queries Make more of the optimizer cost-based 51

configurable cost constants for different storage technologies Provide a program that could

52 New Storage Technologies Time to do a table scan of 10 million records: Memory SSD Hard disk 5 s s s Adjust cost model to support different storage technologies Provide configurable cost constants for different storage technologies Provide a program that could measure performance and suggest good cost constant configuration for a running MySQL server? 52

53 Memory Buffer Aware Cost Estimates Storage engines: Estimate for how much of data and indexes are in a memory buffer Estimate for hit rate for memory buffer Optimizer cost model: Take into account whether data is already in memory or need to be read from disk Server Database buffer Query executor Disk data Storage engine 53

54 Histograms Column statistics Provides the optimizer with information about column value distribution To create/recalculate histogram for a column: ANALYZE TABLE table UPDATE HISTOGRAM ON column WITH n BUCKETS; May use sampling Sample size is based on available memory (histogram_generation_max_mem_size) Automatically chooses between two histogram types: Singleton: One value per bucket Equi-height: Multiple value per bucket 58

55 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 62

56 Hints: Join Order Hints to control table order for join execution 5.7: STRAIGHT_JOIN to force the listed order in FROM clause 8.0: JOIN_FIXED_ORDER /* replacement for STRAIGHT_JOIN*/ JOIN_ORDER /* use specified order */ JOIN_PREFIX /* use specified order for first tables */ JOIN_SUFFIX /* use specified order for last tables */ No need to reorganize the FROM clause to add join order hints like you will for STRAIGHT_JOIN 63

57 Hints: Index Merge Index merge: Merge rows from multiple range scans on a single table Algorithms: union, intersection, sort union Users can specify which indexes to use for index merge /*+ INDEX_MERGE() */ /*+ NO_INDEX_MERGE() */ INDEX(a) SELECT * FROM t1 WHERE a=10 AND b=10 10 INDEX(b) 10 Intersection Result: a=10 AND b=10 65

58 Index Merge Hint - Example EXPLAIN SELECT count(*) FROM users WHERE user_type=2 AND status=0 AND parent_id=0; id select type table type possible keys key key len ref rows Extra Low selectivity 1 SIMPLE users index_ merge parent_id, status, user_type user_type, status, parent_id 1,1,4 NULL 2511 Using intersect (user_type, status, parent_id); Using where; Using index mysql> SELECT count(*) FROM users WHERE user_type=2 AND status=0 AND parent_id=0;... 1 row in set (1.37 sec) mysql> SELECT /*+ INDEX_MERGE(users user_type, status) */ count(*) -> FROM users WHERE user_type=2 AND status=0 AND parent_id=0;... 1 row in set (0.18 sec) 66

59 Hints: Set session variables Set a session variable for the duration of a single statement Examples: SELECT /*+ SET_VAR(sort_buffer_size = 16M) */ name FROM people ORDER BY name; INSERT /*+ SET_VAR(foreign_key_checks = OFF) */ INTO t2 VALUES (1, 1), (2, 2), (3, 3); SELECT /*+ SET_VAR(optimizer_switch = 'condition_fanout_filter = off') */ * FROM customer JOIN orders ON c_custkey = o_custkey WHERE c_acctbal < 0 AND o_orderdate < ' '; NB! Not all session variables are settable through hints: mysql> SELECT /*+ SET_VAR(max_allowed_packet=128M) */ * FROM t1; Empty set, 1 warning (0,01 sec) Warning (Code 4537): Variable 'max_allowed_packet' cannot be set using SET_VAR hint. 67

60 Program Agenda Common table expressions Window functions UTF8 support GIS SKIP LOCKED, NOWAIT JSON functions Index extensions Cost model Hints Better IPv6 and UUID support 68

61 Improved Support for UUID mysql> select uuid(); uuid() aab5d5fd-70c1-11e5-a4fb-b026b977eb Five - separated hexadecimal numbers MySQL uses version 1, the first three numbers are generated from the low, middle, and high parts of a timestamp. 36 characters, inefficient for storage Convert to BINARY(16) datatype, only 16 bytes 69

62 Improved Support for UUID Functions to convert UUID to and from binary UUID_TO_BIN(string_uuid, swap_flag) BIN_TO_UUID(binary_uuid, swap_flag) IS_UUID(string_uuid) Feature Request from Developers 70

63 UUID_TO_BIN Optimization Binary format is now smaller Shuffling low part with the high part improves index performance From VARCHAR(36) To BINARY(16) 53303f87-78fe-11e6-a477-8c89a52c4f3b 11e678fe53303f87a4778c89a52c4f3b Insert Performance Optimized Original 71

64 IPv6 New! Bit-wise operations on binary data types Designed with IPv6 in mind: INET6_ATON(address) & INET6_ATON(network) No longer truncation beyond 64 bits Feature Request from Developers 72

65 All These Features and More Improved Query Scan Performance GROUPING() Hint: Merge/materialize derived table/view JSON: Improved performance of sorting and grouping of JSON values Path expressions: Array index ranges JSON_MERGE_PATCH() Unicode support for reg. exp. Skip index dives for FORCE INDEX Parser Refactoring Try it out! Give us feedback! 73

66 Safe Harbor Statement The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle. 74

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Combining SQL and NoSQL with MySQL

Combining SQL and NoSQL with MySQL Manyi Lu Director MySQL Optimizer Team, Oracle April, 2018 Copyright 2016, 2014, Oracle and/or its affiliates. All rights reserved. Safe Harbor Statement The following